Python for Information Professionals
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Python for Information Professionals
Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience.
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. Unlike many Python books available today that focus on the language from a general perspective, this book is specifically crafted for information professionals seeking to advance their career prospects or embrace new challenges by acquiring skills in the rapidly expanding field of data science.
Readers of Python for Information Professionals will learn to:
- Develop Python applications for the retrieval, cleaning, and analysis of large datasets.
- Design applications to support traditional library functions and create new opportunities to maximise library value.
- Consider data security and privacy relevant to data analysis when using the Python language.
Book Hero Magic summarised reviews for this book. While it's new and still learning, it may not be perfect - your feedback is welcome! HOW HAS THIS BEEN REVIEWED?
Python for Information Professionals offers an accessible introduction to computational programming for information professionals. While best suited for readers with some prior programming familiarity, it provides a clear path to engaging with Python. The tone is approachable and free from typical programming manual clichรฉs, encouraging readers to further explore coding's potential in library work. Recommended for graduate students, faculty, and professionals.
Book Details
INFORMATION
ISBN: 9781538178256
Publisher: Bloomsbury Publishing Plc
Format: Paperback / softback
Date Published: 01 November 2023
Country: United States
Imprint: Rowman & Littlefield
Illustration: 9 b/w photos; 2 tables
Audience: Professional and scholarly
DIMENSIONS
Spine width: 12.0mm
Width: 151.0mm
Height: 228.0mm
Weight: 277g
Pages: 172
About the Author
Brady Lund, Ph.D., is an assistant professor of information science at the University of North Texas. He has published four books related to technology in libraries and educational institutions โ including Casting Light on the Dark Web and Creating Accessible Online Instruction Using Universal Design Principles, both for Rowman and Littlefield Publishing โ and nearly 100 articles, editorials, and opinion papers. His work often combines data analytics principles with library and information science research topics.
Daniel Agbaji is a Ph.D. student in information science at the University of North Texas, with a major in Data Science-Artificial Intelligence and Machine Learning. As an experienced researcher and software developer, he has written scholarly publications and book chapters with notable publishers. Daniel has published articles in the information science and library field. As a software developer, Daniel has written thousands of lines of code for fortune 500 companies which are not publicly available due to company policies.
Kossi Dodzi Bissadu is a Ph.D. student in the computer science at the University of North Texas. He currently works as a software engineer at Zenner USA where he leads various products, software, applications, and systems development projects. He is also a US Air Force veteran, very talented and dedicated professional who has more than ten-year professional record achievements, and demonstrated success leading, managing, and working in Technology and Sciences. Kossi has several industry certifications including certified blockchain developer, AWS certified cloud practitioner, and CompTIA Security+.
Haihua Chen, Ph.D., is an assistant professor of information science at the University of North Texas. He has more than ten years of experience in Python and five years of experience in teaching technical courses for information science and data science students using Python. Dr. Chen has published nearly 40 articles on natural language processing, machine learning, data quality, information retrieval, digital libraries, and applied data science. He is the editor of The Electronic Library and the leading guest editor of Frontiers in Big Data and Information Discovery & Delivery special issues. He is also serving as the reviewer/ PC member for more than 20 peer-review journals/ conferences in information science and computer science.
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